Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition

TL;DR

This paper proposes a biometric identity verification system combining PCA and LDA for feature extraction, aiming to improve robustness and accuracy in real-time applications, especially in high-dimensional image spaces.

Contribution

It introduces a novel combination of PCA and LDA with KNN for biometric recognition, addressing high-dimensional challenges and real-time implementation.

Findings

Effective dimensionality reduction achieved

Improved pattern recognition accuracy

Successful real-time implementation

Abstract

Robustness of embedded biometric systems is of prime importance with the emergence of fourth generation communication devices and advancement in security systems This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional image space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Principal Component Analysis and Lindear Discriminant Analysis with K-Nearest Neighbor and implementing such system in real-time using SignalWAVE.